@inproceedings{0125fb4181e84209a1f5e63c03ba353a,
title = "Planematch: patch coplanarity prediction for robust RGB-D reconstruction",
abstract = "We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction. The core of our method is a deep convolutional neural network that takes in RGB, depth, and normal information of a planar patch in an image and outputs a descriptor that can be used to find coplanar patches from other images. We train the network on 10 million triplets of coplanar and non-coplanar patches, and evaluate on a new coplanarity benchmark created from commodity RGB-D scans. Experiments show that our learned descriptor outperforms alternatives extended for this new task by a significant margin. In addition, we demonstrate the benefits of coplanarity matching in a robust RGBD reconstruction formulation. We find that coplanarity constraints detected with our method are sufficient to get reconstruction results comparable to state-of-the-art frameworks on most scenes, but outperform other methods on established benchmarks when combined with traditional keypoint matching.",
keywords = "Co-planarity, Loop closure, RGB-D registration",
author = "Yifei Shi and Kai Xu and Matthias Nie{\ss}ner and Szymon Rusinkiewicz and Thomas Funkhouser",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2018",
doi = "10.1007/978-3-030-01237-3_46",
language = "English (US)",
isbn = "9783030012366",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "767--784",
editor = "Vittorio Ferrari and Cristian Sminchisescu and Yair Weiss and Martial Hebert",
booktitle = "Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings",
address = "Germany",
}